BuddyBridge

Problem: Students often struggle to find the right person to learn with — someone who matches their goals, skills, or learning style. This lack of meaningful academic connection reduces motivation and slows down learning.

Solution: BuddyBridge is an AI-powered web app that connects students with ideal study partners or mentors in real-time. By simply entering what they want to learn and what they can teach, users are instantly matched with others who complement their skills and interests. A smart matching algorithm, combined with an easy chat feature, makes building meaningful academic connections quick and effortless.

How We Built It: We built BuddyBridge using Next.js (React framework) for the frontend, styled with Tailwind CSS for rapid, responsive design. Firebase handles user authentication and stores user skill profiles in a real-time database. The matching algorithm is a lightweight AI-based scoring model that calculates compatibility based on selected learning and teaching skills. Vercel was used for instant deployment and hosting.

Challenges: One major challenge was designing a matching system that feels natural and accurate with limited input data. Balancing simplicity and usefulness in the user experience was another challenge — too many options confuse users; too few make it ineffective. Time management within the 48-hour window was also critical.

Built With

• css
• firebase
• javascript
• next.js
• openai
• tailwind
• vercel